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WO2019061658A1 - Procédé et dispositif de localisation de lunettes, et support d'informations - Google Patents

Procédé et dispositif de localisation de lunettes, et support d'informations Download PDF

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Publication number
WO2019061658A1
WO2019061658A1 PCT/CN2017/108756 CN2017108756W WO2019061658A1 WO 2019061658 A1 WO2019061658 A1 WO 2019061658A1 CN 2017108756 W CN2017108756 W CN 2017108756W WO 2019061658 A1 WO2019061658 A1 WO 2019061658A1
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Prior art keywords
glasses
image
sample
classifier
real
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Ceased
Application number
PCT/CN2017/108756
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English (en)
Chinese (zh)
Inventor
戴磊
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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Priority to US16/337,938 priority Critical patent/US10635946B2/en
Publication of WO2019061658A1 publication Critical patent/WO2019061658A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components

Definitions

  • the present application relates to the field of computer vision processing technologies, and in particular, to a glasses positioning method, an electronic device, and a computer readable storage medium.
  • the face images with deep-frame glasses are highly similar in face recognition, and accurate face recognition cannot be performed.
  • the method adopted in the industry is to remove the face image in the face image and then recognize the face image after removing the eyeglass region.
  • the key to this method is how to accurately determine the area of the glasses in the face image.
  • early glasses detection mainly adopts image processing and template matching methods, detecting the lower border of the glasses and the nose bridge of the glasses according to the discontinuous change of the gray value of the pixels, and then detecting the glasses through the edge information of the area between the two eyes;
  • the glasses detection mainly uses the three-dimensional Hough transform method to detect the glasses.
  • the image obtained by image processing and Hough method after imaging is too dependent on the edge of the image, so there is noise, and noise interference may often result in the inability to obtain feature points or accurate feature points, so the accuracy of detection Relatively low.
  • the present application provides a glasses positioning method, an electronic device, and a computer readable storage medium, the main purpose of which is to improve the accuracy of positioning glasses in a face image.
  • the present application provides an electronic device, including: a memory, a processor, and an imaging device, wherein the memory includes a glasses positioning program, and the glasses positioning program is executed by the processor to implement the following steps:
  • the position of the glasses in the real-time facial image is located by using a predetermined second classifier, and the positioning result is output.
  • the present application further provides a method for positioning glasses, the method comprising:
  • the position of the glasses in the real-time facial image is located by using a predetermined second classifier, and the positioning result is output.
  • the present application further provides a computer readable storage medium including a glasses positioning program, and when the glasses positioning program is executed by a processor, realizing the positioning of the glasses as described above Any step in the method.
  • the eyeglass positioning method, the electronic device and the computer readable storage medium provided by the present application first determine whether the face image includes the glasses through the first classifier, and then input the face image including the glasses into the second classifier to determine the person.
  • the position of the glasses in the face image The present invention uses two classifiers to detect the image of the eyeglass area in the face image, and does not depend on the edge of the image, thereby improving the accuracy and accuracy of the eyeglass detection.
  • FIG. 1 is a schematic diagram of hardware of a preferred embodiment of an electronic device of the present application.
  • FIG. 2 is a schematic block diagram of a preferred embodiment of the glasses positioning program of FIG. 1;
  • FIG. 3 is a flow chart of a preferred embodiment of a method for positioning glasses according to the present application.
  • the application provides an electronic device 1 .
  • 1 is a hardware schematic diagram of a preferred embodiment of an electronic device of the present application.
  • the electronic device 1 may be a terminal device having a computing function, such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.
  • a computing function such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.
  • the electronic device 1 may be a server having a glasses positioning program, a smart phone, a tablet computer, a portable computer, a desktop computer, and the like having a computing function
  • the server may be a rack server. Blade server, tower server, or rack server.
  • the electronic device 1 includes a memory 11, a processor 12, an imaging device 13, a network interface 14, and a communication bus 15.
  • the memory 11 includes at least one type of readable storage medium.
  • the at least one type of readable storage medium may be, for example, a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or A non-volatile storage medium such as a DX memory or the like, a magnetic memory, a magnetic disk, or an optical disk.
  • the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1.
  • the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), and a secure digital ( Secure Digital, SD) cards, flash cards, etc.
  • SMC smart memory card
  • Secure Digital Secure Digital
  • the readable storage medium of the memory 11 is generally used to store the glasses positioning program 10 installed in the electronic device 1, the predetermined first classifier, the model file of the second classifier, and various types. Data, etc.
  • the memory 11 can also be used to temporarily store data that has been output or is about to be output.
  • the processor 12 in some embodiments, may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing a glasses positioning procedure. 10 and so on.
  • CPU Central Processing Unit
  • microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing a glasses positioning procedure. 10 and so on.
  • the imaging device 13 may be part of the electronic device 1 or may be independent of the electronic device 1.
  • the electronic device 1 is a terminal device having a camera such as a smartphone, a tablet computer, a portable computer, etc.
  • the camera device 13 is a camera of the electronic device 1.
  • the electronic device 1 may be a server, and the camera device 13 is connected to the electronic device 1 via a network, for example, the camera device 13 is installed in a specific place, such as an office. And monitoring the area, real-time image is taken in real time for the target entering the specific place, and the captured real-time image is transmitted to the processor 12 through the network.
  • the network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the electronic device 1 and other electronic devices.
  • a standard wired interface such as a WI-FI interface
  • Communication bus 15 is used to implement connection communication between these components.
  • Figure 1 shows only the electronic device 1 with components 11-15, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the electronic device 1 may further include a user interface, and the user interface may include an input unit such as a keyboard, etc., optionally, the user interface may further include a standard wired interface and a wireless interface.
  • the electronic device 1 may further include a display, which may also be appropriately referred to as a display screen or a display unit.
  • a display may also be appropriately referred to as a display screen or a display unit.
  • it may be an LED display, a liquid crystal display, a touch liquid crystal display, and an Organic Light-Emitting Diode (OLED) touch sensor.
  • the display is used to display information processed in the electronic device 1 and a user interface for displaying visualizations.
  • the electronic device 1 may further include a touch sensor.
  • the area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area.
  • the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like.
  • the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like.
  • the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array.
  • the area of the display of the electronic device 1 may be the same as the area of the touch sensor. It can also be different.
  • a display is stacked with the touch sensor to form a touch display. The device detects a user-triggered touch operation based on a touch screen display.
  • the electronic device 1 may further include a radio frequency (RF) circuit, a sensor, an audio circuit, and the like, and details are not described herein.
  • RF radio frequency
  • a storage unit 10 is stored in a memory 11 as a computer storage medium.
  • the processor 12 executes the glasses positioning program 10 stored in the memory 11, the following steps are implemented:
  • the position of the glasses in the real-time facial image is located by using a predetermined second classifier, and the positioning result is output.
  • the camera 13 When the camera 13 captures a real-time image, the camera 13 transmits the real-time image to the processor 12, and the processor 12 receives the real-time image and acquires the size of the real-time image to create a grayscale image of the same size.
  • the acquired color image is converted into a grayscale image, and a memory space is created at the same time; the grayscale image histogram is equalized, the amount of grayscale image information is reduced, the detection speed is accelerated, and then the training library is loaded to detect the face in the image. And return an object containing face information, obtain the data of the location of the face, and record the number; finally get the face area and save it, thus completing the process of face image extraction.
  • the face recognition algorithm for extracting the face image from the real-time image may be a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, a neural network method, or the like.
  • the face image extracted by the face recognition algorithm is input to a predetermined first classifier, and it is determined whether the face image includes glasses.
  • the training step of the predetermined first classifier includes:
  • the picture is used as a training set, and the second proportion of sample pictures are randomly extracted from the remaining first sample set as a verification set, for example, 50%, that is, 25% of the sample pictures in the first sample set are used as a verification set, and the training is utilized.
  • the training convolutional neural network is set to obtain the first classifier; in order to ensure the accuracy of the first classifier, the accuracy of the first classifier needs to be verified, and the first classification of the training is verified by using the verification set Accuracy of the device, if the accuracy is greater than or equal to the preset accuracy, the training ends, or if the accuracy is less than the preset accuracy, the sample in the sample set is increased. Number of pictures and re-execute the above steps.
  • the training step of the predetermined first classifier further includes: performing preprocessing such as scaling, cropping, flipping, and/or twisting on the sample image in the first sample set, and utilizing
  • preprocessing such as scaling, cropping, flipping, and/or twisting
  • the pre-processed sample images train the convolutional neural network to effectively improve the authenticity and accuracy of the model training.
  • performing image preprocessing on each sample picture may include:
  • each predetermined preset type parameter for example, a corresponding standard parameter value such as color, brightness and/or contrast
  • the standard parameter value corresponding to the color is a1
  • the standard parameter value corresponding to the brightness is a2
  • the standard parameter corresponding to the contrast The value is a3
  • each predetermined preset type parameter value of each second picture is adjusted to a corresponding standard parameter value, and a corresponding third picture is obtained, so as to eliminate the unclear picture caused by external conditions of the sample picture when shooting.
  • each fourth picture is a training picture of the corresponding sample picture.
  • the function of the flip and twist operation is to simulate various forms of pictures in the actual business scene. Through these flip and twist operations, the size of the data set can be increased, thereby improving the authenticity and practicability of the model training.
  • the first classifier trained by the above steps determines that the face image includes glasses, and then inputs the face image into a predetermined second classifier, locates the glasses area in the face image, and outputs the The positioning result of the glasses in the face image. It can be understood that, if the eyeglass region is not included in the face image in the determination result output by the first classifier, the real-time image captured by the camera device 13 is reacquired, and the subsequent steps are performed.
  • the predetermined second classifier acquiring process is as follows: preparing a preset number of "glasses” sample pictures to form a second sample set, and in other embodiments, using the first sample A sample picture with "glasses” or "1" is marked in the group.
  • image preprocessing is performed on each sample picture. Specifically, the preprocessing step includes: converting a sample picture in the second sample set from a color image to a gray image, and then pixel points in the gray image.
  • the pixel values are respectively divided by 255, and the pixel values of each pixel point are ranged from 0-255 to 0-1; the position of the glasses in the sample picture after the above pre-processing is marked with a preset number of marked points, For example, eight feature points are marked on the eyeglass frame in each sample picture: the upper and lower frames are uniformly labeled with three feature points, and the left and right frame edges are respectively labeled with one feature point.
  • a preset number of marked points representing the position of the glasses in each sample picture are combined into one vector, and the vector of one sample picture is used as a reference vector, and the remaining m-
  • the vector of one sample picture is aligned with the reference vector to obtain a first average model for the position of the glasses; the first average model for the position of the glasses is subjected to dimensionality reduction by Principal Components Analysis (PCA).
  • PCA Principal Components Analysis
  • the local feature of each marker point is extracted from the second average model using a feature extraction algorithm, and the second average model for the position of the glasses and the local features of each of the marker points are used as the second classifier.
  • the feature extraction algorithm is a SIFT (scale-invariant feature transform) algorithm, and the SIFT algorithm extracts local features of each feature point from the second average model, selects a feature point as a reference feature point, and searches for A feature point that is the same as or similar to a local feature of the reference feature point (eg, the difference of the local features of the two feature points is within a preset range), according to this principle until all lip feature points are found.
  • SIFT scale-invariant feature transform
  • the feature extraction algorithm may also be a SURF (Speeded Up Robust Features) algorithm, an LBP (Local Binary Patterns) algorithm, a HOG (Histogram of Oriented Gridients) algorithm, or the like.
  • SURF Speeded Up Robust Features
  • LBP Long Binary Patterns
  • HOG Histogram of Oriented Gridients
  • the electronic device 1 proposed in this embodiment first determines whether the face image is included in the face image by the first classifier, and then inputs the face image including the glasses into the second classifier to determine the position of the glasses in the face image.
  • the accuracy and accuracy of the glasses detection are improved.
  • the glasses positioning program 10 can also be partitioned into one or more modules, one or more modules being stored in the memory 11 and executed by the processor 12 to complete the application.
  • a module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function.
  • FIG. 2 it is a block diagram of the glasses positioning program 10 of FIG.
  • the glasses positioning program 10 can be divided into: an obtaining module 110, a determining module 120, and a positioning module 130.
  • the functions or operating steps implemented by the modules 110-130 are similar to the above, and are not described in detail herein. Sexually, for example:
  • the acquiring module 110 is configured to acquire a real-time image captured by the camera device 13 and extract a real-time facial image from the real-time image by using a face recognition algorithm;
  • the determining module 120 is configured to identify, by using a predetermined first classifier, whether the real-time facial image includes glasses, and output the recognition result;
  • the positioning module 130 is configured to: when the recognition result is that the real-time facial image includes glasses, locate the glasses position in the real-time facial image by using a predetermined second classifier, and output the positioning result.
  • the present application also provides a method for positioning glasses.
  • FIG. 3 it is a flowchart of the first embodiment of the glasses positioning method of the present application. The method can be performed by a device that can be implemented by software and/or hardware.
  • the glasses positioning method includes steps S10-S30:
  • Step S10 acquiring a real-time image captured by the camera device, and extracting a real-time face image from the real-time image by using a face recognition algorithm;
  • Step S20 using a predetermined first classifier to identify whether the real-time facial image includes glasses, and outputting the recognition result;
  • Step S30 when the recognition result is that the real-time facial image includes glasses, the predetermined The second classifier positions the position of the glasses in the real-time face image and outputs the positioning result.
  • the camera When the camera captures a real-time image, the camera sends the real-time image to the processor, and the processor receives the real-time image and obtains the size of the real-time image, and creates a gray image of the same size, and the acquired color image Converting to a grayscale image, creating a memory space at the same time; equalizing the grayscale image histogram, reducing the amount of grayscale image information, speeding up the detection speed, then loading the training library, detecting the face in the image, and returning an inclusion
  • the object of the face information obtains the data of the location of the face and records the number; finally, the area of the face is obtained and saved, thus completing the process of extracting the face image.
  • the face recognition algorithm for extracting the face image from the real-time image may be a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, a neural network method, or the like.
  • the face image extracted by the face recognition algorithm is input to a predetermined first classifier, and it is determined whether the face image includes glasses.
  • the training step of the predetermined first classifier includes:
  • the picture is used as a training set, and the second proportion of sample pictures are randomly extracted from the remaining first sample set as a verification set, for example, 50%, that is, 25% of the sample pictures in the first sample set are used as a verification set, and the training is utilized.
  • the training convolutional neural network is set to obtain the first classifier; in order to ensure the accuracy of the first classifier, the accuracy of the first classifier needs to be verified, and the first classification of the training is verified by using the verification set Accuracy of the device, if the accuracy is greater than or equal to the preset accuracy, the training ends, or if the accuracy is less than the preset accuracy, the sample in the sample set is increased. Number of pictures and re-execute the above steps.
  • the training step of the predetermined first classifier further includes: performing preprocessing such as scaling, cropping, flipping, and/or twisting on the sample image in the first sample set, and using the preprocessed
  • preprocessing such as scaling, cropping, flipping, and/or twisting
  • performing image preprocessing on each sample picture may include:
  • each predetermined preset type parameter for example, a corresponding standard parameter value such as color, brightness and/or contrast
  • the standard parameter value corresponding to the color is a1
  • the standard parameter value corresponding to the brightness is a2
  • the standard parameter corresponding to the contrast The value is a3
  • each predetermined preset type parameter value of each second picture is adjusted to a corresponding standard parameter value, and a corresponding third picture is obtained, so as to eliminate the unclear picture caused by external conditions of the sample picture when shooting.
  • each third picture is warped according to a preset twist angle (for example, 30 degrees), and a fourth picture corresponding to each third picture is obtained, and each fourth picture is a training picture of the corresponding sample picture.
  • the function of the flip and twist operation is to simulate various forms of pictures in the actual business scene. Through these flip and twist operations, the size of the data set can be increased, thereby improving the authenticity and practicability of the model training.
  • the first classifier trained by the above steps determines that the face image includes glasses, and then inputs the face image into a predetermined second classifier, locates the glasses area in the face image, and outputs the The positioning result of the glasses in the face image. It can be understood that, if the eyeglass region is not included in the face image in the determination result output by the first classifier, the real-time image captured by the camera device 13 is reacquired, and the subsequent steps are performed.
  • the predetermined second classifier acquiring process is as follows: preparing a preset number of "glasses” sample pictures to form a second sample set, and in other embodiments, using the first sample A sample picture with "glasses” or "1" is marked in the group.
  • image preprocessing is performed on each sample picture. Specifically, the preprocessing step includes: converting a sample picture in the second sample set from a color image to a gray image, and then pixel points in the gray image.
  • the pixel values are respectively divided by 255, and the pixel values of each pixel point are ranged from 0-255 to 0-1; the position of the glasses in the sample picture after the above pre-processing is marked with a preset number of marked points, For example, eight feature points are marked on the eyeglass frame in each sample picture: the upper and lower frames are uniformly labeled with three feature points, and the left and right frame edges are respectively labeled with one feature point.
  • a preset number of marked points representing the position of the glasses in each sample picture are combined into one vector, and the vector of one sample picture is used as a reference vector, and the remaining m- A vector of one sample picture is aligned with the reference vector to obtain a first average model for the position of the glasses; a PCA dimensionality reduction process is performed on the first average model for the position of the glasses to obtain a second average model for the position of the glasses.
  • a local feature of each marker point is extracted from the second average model using a feature extraction algorithm, and a second average type for the position of the glasses and a local feature of each of the marker points are used as the second classifier.
  • the feature extraction algorithm is a SIFT algorithm, and the SIFT algorithm extracts a local feature of each feature point from the second average model, selects a feature point as a reference feature point, and searches for a feature that is identical or similar to the local feature of the reference feature point.
  • the point for example, the difference between the local features of the two feature points is within a preset range), according to this principle until all lip feature points are found.
  • the feature extraction algorithm may also be a SURF algorithm, an LBP algorithm, an HOG algorithm, or the like.
  • the first classifier is used to determine whether the face image includes glasses, and then the face image including the glasses is input to the second classifier to determine the position of the glasses in the face image.
  • the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a glasses positioning program, and the glasses positioning program is implemented by the processor to implement the following operating:
  • the position of the glasses in the real-time facial image is located by using a predetermined second classifier, and the positioning result is output.
  • the training process of the predetermined first classifier is as follows:
  • the sample image after the classification mark is divided into a training set of a first ratio and a verification set of a second ratio;
  • the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the number of sample pictures and Re-execute the training steps.
  • the obtaining process of the predetermined second classifier is as follows:
  • a local feature of each marker point is extracted from the second average model, and a second average model for the position of the glasses and a local feature of each of the marker points are used as the second classifier.

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Abstract

L'invention concerne un procédé de localisation de lunettes, un dispositif électronique (1) et un support d'informations lisible par ordinateur. Le procédé comprend les étapes qui consistent : à acquérir une image en temps réel capturée par un dispositif de photographie (13), et à employer un algorithme de reconnaissance faciale pour extraire de l'image en temps réel une image de visage en temps réel (S10) ; à utiliser un premier classifieur prédéfini pour déterminer si l'image de visage en temps réel contient des lunettes, et à émettre un résultat de reconnaissance (S20) ; si le résultat de reconnaissance indique que l'image de visage en temps réel contient des lunettes, à utiliser un second classifieur prédéfini pour localiser les lunettes dans l'image de visage en temps réel, et à émettre un résultat de localisation (S30). L'invention se sert de deux classifieurs pour détecter une région de lunettes dans une image de visage, ce qui permet d'accroître la précision et l'exactitude de la détection de lunettes.
PCT/CN2017/108756 2017-09-30 2017-10-31 Procédé et dispositif de localisation de lunettes, et support d'informations Ceased WO2019061658A1 (fr)

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US16/337,938 US10635946B2 (en) 2017-09-30 2017-10-31 Eyeglass positioning method, apparatus and storage medium

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CN201710915085.X 2017-09-30
CN201710915085.XA CN107808120B (zh) 2017-09-30 2017-09-30 眼镜定位方法、装置及存储介质

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CN112346862A (zh) * 2020-10-27 2021-02-09 上海影创信息科技有限公司 分体式智能眼镜控制方法、系统及介质
CN112926439A (zh) * 2021-02-22 2021-06-08 深圳中科飞测科技股份有限公司 检测方法及装置、检测设备和存储介质

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